A flight operation data statistical method and device based on multi-source fusion

Abstract

A kind of flight operation data statistical method and device based on multi-source fusion, according to the statistical condition of preset to the statistical processing of national flight operation situation data, including the following steps: setting statistical condition, setting statistical mode, setting statistical index, establishing fusion index library, the relevant data of different sources is fused to unified index library platform, for the same data content of different sources, through setting dynamic threshold floating range, the correlation matching of flight is realized, according to statistical condition, mode and index setting, data retrieval, processing and statistical operation are carried out, and statistical result is output.The present application realizes multi-source data fusion, according to the actual demand of user, freely combines statistical condition, flexibly selects statistical mode and index, can adapt to the statistical needs of multiple scenes, and efficiently outputs statistical result, improves the statistical accuracy and statistical efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of civil aviation data statistics, and in particular to a method and apparatus for efficient and accurate statistical analysis of flight data based on multi-source fusion, which flexibly sets statistical requirements to achieve the analysis of flight operation data nationwide. Background Technology

[0002] In the current civil aviation system, different civil aviation units, such as air traffic control, airlines, and airports, possess data on different aspects of their operations. This data is often stored and managed using different formats and structures. For example, air traffic control systems focus on flight operation monitoring and air traffic management data, while airlines pay more attention to data related to flight operations and passenger services. Airports manage data on various aspects, including flight information, passenger flow, security checks, and ground services. During civil aviation data statistics, extracting data from different systems is necessary to analyze different flight operations. Furthermore, the structural differences in these data necessitate complex data conversion and standardization processes during statistical analysis. Inconsistencies may also exist between different data sources; for instance, airports and airlines often have different departure times for the same flight, requiring dynamic adjustments during the statistical process. These combined factors result in existing statistical methods suffering from high labor costs, significant biases in statistical results, and low efficiency.

[0003] With the rapid development of the aviation industry, the massive increase in flight operation data has placed higher demands on statistical analysis. Traditional statistical methods, limited by fixed statistical frameworks, are increasingly unable to meet diverse analytical needs. Therefore, it is particularly important to develop a flight operation statistics method that can adapt to multiple scenarios, achieve multi-source data fusion, allow users to freely combine statistical conditions, flexibly select statistical modes and indicators according to their actual needs, and accurately and efficiently output statistical results. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a multi-source fusion-based method for statistical analysis of flight operation data. This method can adapt to the statistical needs of various flight operation data in multiple scenarios, reducing labor costs and improving statistical analysis efficiency.

[0005] The flight operation data statistical method based on multi-source fusion of the present invention includes the following steps:

[0006] (1) Set statistical conditions, validate the statistical conditions input by the user, and convert content that does not meet the format requirements into a format that is recognized by the computer program;

[0007] (2) Set the statistical mode;

[0008] (3) Set statistical indicators and establish a fusion indicator library to integrate relevant data from different sources into a unified indicator library platform. For cases where the same data content from different sources is inconsistent, flight association matching can be achieved by setting a dynamic threshold fluctuation range.

[0009] (4) Based on the statistical conditions, statistical modes and statistical indicator requirements set by the user, extract relevant flight operation data from the database for retrieval, processing and statistical calculation;

[0010] (5) Output of statistical results.

[0011] Furthermore, the statistical conditions set in step (1) include the following:

[0012] Basic statistical conditions are set, including date range, passenger / cargo nature, mission nature, plan type, nationality / region, and aircraft type;

[0013] Airline criteria settings include airline code, country of origin, continent of origin, other attributes, and ranking settings;

[0014] The statistical criteria for airports and cities include airport codes, airport attributes, region, province, continent, continental sub-region, country, city, and ranking settings.

[0015] Furthermore, setting the statistical mode in step (2) includes the following:

[0016] Date range statistical mode settings; airline statistical mode settings; airport / city pair statistical mode settings; passenger and cargo statistical mode settings; mission nature statistical mode settings; plan type statistical mode settings; nationality / region statistical mode settings.

[0017] Furthermore, the statistical indicators set in step (3) include the following:

[0018] Flight volume metric settings; On-time performance metric settings; Average delay time metric settings; Cancellation rate metric settings.

[0019] Furthermore, the flight association matching method in step (3) includes the following:

[0020] Identify the key fields used for matching, including flight number, scheduled date, departure time, arrival time, departure airport, and arrival airport information;

[0021] A threshold range is set for takeoff time, and this threshold range can be dynamically adjusted according to the matching situation;

[0022] Flight data is matched based on flight number, scheduled date, departure airport, and arrival airport. If there are multiple flights to be matched, the departure time is determined according to a set dynamic threshold range. Flight data with departure times within the set threshold range is confirmed, while flight data with departure times outside the set threshold range is not confirmed. Matching flights is confirmed in this way.

[0023] Furthermore, the flight association matching method in step (3) also includes the following:

[0024] The accuracy of flight matching results is verified through manual review or comparison with other reliable data sources, and the threshold fluctuation range is dynamically adjusted based on the verification results.

[0025] Furthermore, in step (2) of setting the statistical mode, a pattern library containing a variety of common statistical modes is established in advance, providing a variety of preset statistical modes for users to choose from.

[0026] Furthermore, users can customize the settings for statistical conditions, statistical modes, and statistical indicators.

[0027] The present invention also provides an apparatus for implementing the above method, comprising the following modules:

[0028] Input module: Used to receive statistical conditions, statistical modes and statistical indicators input by the user.

[0029] Processing module: Establishes a fusion indicator library, retrieves relevant flight operation data from the database based on the information from the input module, sets dynamic threshold fluctuation ranges to achieve flight association matching, and applies preset or custom statistical algorithms for analysis and processing.

[0030] Output module: Outputs the statistical results obtained by the processing module in a user-specified format, supporting multiple display methods.

[0031] Storage module: Used to store national flight operation data and intermediate and final results generated during statistical analysis.

[0032] The multi-source fusion-based flight operation data statistical method provided by this invention can adapt to the fusion and statistical needs of various flight operation data in multiple scenarios. It can help users freely combine statistical conditions according to actual needs, flexibly select statistical modes and indicators, and perform data transformation and standardization processing to address structural differences in multi-source data. It also performs quality checks and cleaning on various fused data, and sets dynamic threshold fluctuation ranges to achieve flight association matching correction for inconsistencies between different data sources. This not only improves the accuracy of data statistics, but also meets the requirements of data statistical analysis in the context of massive growth in flight operation data, reduces labor costs, and improves statistical analysis efficiency. Attached Figure Description

[0033] Figure 1 This is a flowchart of the flight operation statistics method of the present invention;

[0034] Figure 2 This is a flowchart of step (1) of the flight operation statistics method of the present invention;

[0035] Figure 3 This is a flowchart of step (2) of the flight operation statistics method of the present invention;

[0036] Figure 4 This is a flowchart of step (3) of the flight operation statistics method of the present invention;

[0037] Figure 5 This is a flowchart of step (4) of the flight operation statistics method of the present invention;

[0038] Figure 6 This is a schematic diagram of the flight operation statistics device of the present invention. Detailed Implementation

[0039] like Figure 1 As shown in the flowchart, this invention provides a method for statistical analysis of flight operation data based on multi-source fusion. The specific implementation method is as follows:

[0040] Step (1) provides a user interface to set statistical conditions, allowing users to input or select the desired statistical conditions. The statistical conditions include the following (see...). Figure 2 As shown):

[0041] Basic statistical conditions are set, including date range, passenger / cargo nature, mission nature, plan type, nationality / region, and aircraft type;

[0042] Airline criteria settings include airline code, country of origin, continent of origin, other attributes, and ranking settings;

[0043] The statistical criteria for airports and cities include airport codes, airport attributes, region, province, continent, continental sub-region, country, city, and ranking settings.

[0044] Preferably, a graphical user interface (GUI) is designed to allow users to input or select statistical criteria. These criteria may be presented in the form of text boxes, drop-down menus, checkboxes, etc.

[0045] Input validation: Validate the statistical conditions entered by users to ensure that they are valid and conform to data format requirements, such as the validity of time ranges and the accuracy of airport codes;

[0046] Data preprocessing: For user input that does not meet the format requirements, automatically convert it into a format that the computer's internal programs can understand and operate, such as converting time in string form into date objects.

[0047] Step (2), set the statistical mode, such as Figure 3 As shown, this includes settings for date range statistics, airline statistics, airport / city statistics, passenger / cargo statistics, task nature statistics, plan type statistics, and nationality / region statistics.

[0048] Preferably, a pattern library containing various common statistical patterns is established, providing multiple preset statistical patterns for users to choose from, such as classification by date, analysis by airline, classification by airport or city, classification by passenger and cargo nature, classification by plan type, etc. Each pattern corresponds to a specific data processing logic; a list is provided in the GUI for users to select from the preset pattern library, while also supporting user-defined statistical patterns.

[0049] Step (3), set statistical indicators: such as Figure 4 As shown, a list of available statistical indicators can be set, such as flight volume, on-time rate, delay time, cancellation rate, etc. All available statistical indicators are listed in the GUI, allowing users to select according to their needs. At the same time, users can define their own statistical indicators according to business needs, which can be achieved by combining existing indicators or writing new calculation formulas.

[0050] Establish a unified indicator library: integrate different data from different units into a unified indicator library platform. For example, for data related to flight regularity, integrate flight delay information, flight cancellation information, passenger load factor and other related information. Multi-source data fusion can make full use of the complementarity between different data sources and facilitate the discovery of data errors and conflicts.

[0051] Data inspection and cleaning: Data from different sources may vary in quality, containing issues such as duplication, missing data, inconsistencies, and errors. These problems directly affect the accuracy and reliability of subsequent data statistics. Therefore, in the process of multi-source data fusion, the first step is to conduct rigorous quality checks and cleaning of the data, including removing invalid characters and handling missing values, and ensuring that all flight data are formatted uniformly, such as date, time, and flight number formats, to ensure that the fused data accurately reflects the actual situation.

[0052] To address potential data inconsistencies between different data sources—such as airports and airlines having different departure times for the same flight at a given data collection point—a threshold-based dynamic adjustment flight data matching algorithm needs to be employed during the fusion process. This involves setting a dynamic threshold fluctuation range to achieve flight association and matching. Specific steps include:

[0053] Identify the key fields used for matching, including flight number, scheduled date, departure time, arrival time, departure airport, and arrival airport information;

[0054] Set a threshold range for takeoff time, such as 120 minutes. This threshold range can be dynamically adjusted according to the matching situation.

[0055] Flight data is matched based on flight number, scheduled date, departure airport, and arrival airport. If there are multiple flights to be matched, the departure time is determined according to a set dynamic threshold range. Flight data with departure times within the set threshold range is confirmed, while flight data with departure times outside the set threshold range is not confirmed. Matching flights is confirmed in this way.

[0056] The accuracy of flight matching results is verified through manual review or comparison with other reliable data sources, and the threshold fluctuation range is dynamically adjusted based on the verification results.

[0057] Step (4), data statistical analysis: such as Figure 5 As shown, based on the statistical conditions, statistical modes, and statistical indicator requirements set by the user, relevant flight operation data is extracted from the database for retrieval, processing, and statistical calculations; including:

[0058] Data retrieval: Based on the statistical conditions and statistical modes set by the user, retrieve relevant flight operation data from the database, and execute corresponding SQL queries or data filtering operations as needed;

[0059] Data processing: Perform necessary processing on the retrieved data, such as deduplication, sorting, and grouping, to meet the requirements of the statistical model;

[0060] Statistical calculation: Based on the statistical indicators selected by the user, perform corresponding statistical calculations on the data, such as summation, average, and ratio calculation.

[0061] Step (5), output statistical results: output the statistical analysis results in the form of charts, reports, etc., and support user-defined output formats for easy viewing and sharing later.

[0062] Results organization: Organize the results obtained from statistical calculations into an easy-to-understand format, such as tables, charts, etc.

[0063] Format selection: The GUI provides options for users to choose their desired output format (such as CSV file, Excel spreadsheet, PDF report, etc.).

[0064] Output generation: Based on the user's selection, statistical results are generated and saved. Relevant detailed information can also be retrieved and exported.

[0065] The invention also includes an apparatus for implementing the aforementioned multi-source fusion-based flight operation data statistics method, such as... Figure 6 As shown, it includes the following modules:

[0066] Input module: Used to receive statistical conditions, statistical modes and statistical indicators input by the user.

[0067] Processing module: Establishes a fusion indicator library, retrieves relevant flight operation data from the database based on the information from the input module, sets dynamic threshold fluctuation ranges to achieve flight association matching, and applies preset or custom statistical algorithms for analysis and processing.

[0068] Output module: Outputs the statistical results obtained by the processing module in a user-specified format, supporting multiple display methods.

[0069] Storage module: Used to store national flight operation data and intermediate and final results generated during statistical analysis.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A flight operation data statistical method based on multi-source fusion, characterized in that, Includes the following steps: (1) Set statistical conditions, validate the statistical conditions input by the user, and convert content that does not meet the format requirements into a format that is recognized by the computer program; (2) Set the statistical mode; (3) Set statistical indicators and establish a fusion indicator library to integrate relevant data from different sources into a unified indicator library platform. For cases where the same data content from different sources is inconsistent, flight association matching can be achieved by setting a dynamic threshold fluctuation range. (4) Based on the statistical conditions, statistical modes and statistical indicator requirements set by the user, extract relevant flight operation data from the database for retrieval, processing and statistical calculation; (5) Output of statistical results. 2.The flight operation data statistical method based on multi-source fusion according to claim 1, wherein, The statistical conditions set in step (1) include the following: Basic statistical conditions are set, including date range, passenger / cargo nature, mission nature, plan type, nationality / region, and aircraft type; Airline criteria settings include airline code, country of origin, continent of origin, other attributes, and ranking settings; The statistical criteria for airports and cities include airport codes, airport attributes, region, province, continent, continental sub-region, country, city, and ranking settings. 3.The flight operation data statistical method based on multi-source fusion according to claim 1, characterized in that, The statistical mode setting in step (2) includes the following: Set the statistical modes for date ranges, airlines, airports and cities, passengers and cargo, mission nature, plan type, and nationality / region.

4. The flight operation data statistical method based on multi-source fusion according to claim 1, characterized in that, The statistical indicators set in step (3) include the following: Flight volume metric settings; On-time performance metric settings; Average delay time metric settings; Cancellation rate metric settings.

5. The flight operation data statistical method based on multi-source fusion according to claim 1 or 4, characterized in that, The flight association matching method in step (3) includes the following: Identify the key fields used for matching, including flight number, scheduled date, departure time, arrival time, departure airport, and arrival airport information; A threshold range is set for takeoff time, and this threshold range can be dynamically adjusted according to the matching situation; Flight data is matched based on flight number, scheduled date, departure airport, and arrival airport. If there are multiple flights to be matched, the departure time is determined according to a set dynamic threshold range. Flight data with departure times within the set threshold range is confirmed, while flight data with departure times outside the set threshold range is not confirmed. Matching flights is confirmed in this way.

6. The flight operation data statistical method based on multi-source fusion according to claim 5, characterized in that, The flight association matching method in step (3) also includes the following: The accuracy of flight matching results is verified through manual review or comparison with other reliable data sources, and the threshold fluctuation range is dynamically adjusted based on the verification results.

7. A method for statistical analysis of flight operation data based on multi-source fusion according to any one of claims 1-6, characterized in that, In step (2) of setting the statistical mode, a pattern library containing a variety of common statistical modes is established in advance, providing a variety of preset statistical modes for users to choose from.

8. The flight operation data statistics method based on multi-source fusion according to any one of claims 1-6, characterized in that, Users can customize the settings when setting statistical conditions, statistical modes, and statistical indicators.

9. An apparatus for implementing the method of any one of claims 1-8, wherein, Includes the following modules: Input module: Used to receive statistical conditions, statistical modes and statistical indicators input by the user. Processing module: Establishes a fusion indicator library, retrieves relevant flight operation data from the database based on the information from the input module, sets dynamic threshold fluctuation ranges to achieve flight association matching, and applies preset or custom statistical algorithms for analysis and processing. Output module: Outputs the statistical results obtained by the processing module in a user-specified format, supporting multiple display methods. Storage module: Used to store national flight operation data and intermediate and final results generated during statistical analysis.

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